L4L: Experience-Driven Computational Resource Control in Federated Learning
IEEE Transactions on Computers(2022)
摘要
As the large-scale deployment of machine learning applications, there is much research attention on exploiting a vast amount of data stored on mobile clients. To preserve data privacy, federated learning has been proposed to enable large-scale machine learning by massive clients without exposing raw data. Existing works of federated learning struggle for accelerating the learning process, but igno...
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关键词
Collaborative work,Training,Computational modeling,Training data,Servers,Data models,Bandwidth
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